2 code implementations • 18 Sep 2018 • Xinnuo Xu, Ondřej Dušek, Ioannis Konstas, Verena Rieser
We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity.
1 code implementation • EMNLP 2018 • Xinnuo Xu, Ond{\v{r}}ej Du{\v{s}}ek, Ioannis Konstas, Verena Rieser
We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity.
1 code implementation • ACL 2021 • Xinnuo Xu, Guoyin Wang, Young-Bum Kim, Sungjin Lee
Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language.
1 code implementation • ACL 2021 • Xinnuo Xu, Ondřej Dušek, Verena Rieser, Ioannis Konstas
We present AGGGEN (pronounced 'again'), a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation.
no code implementations • 20 Dec 2017 • Ioannis Papaioannou, Amanda Cercas Curry, Jose L. Part, Igor Shalyminov, Xinnuo Xu, Yanchao Yu, Ondřej Dušek, Verena Rieser, Oliver Lemon
Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence.
no code implementations • WS 2019 • Xinnuo Xu, Yizhe Zhang, Lars Liden, Sungjin Lee
Although the data-driven approaches of some recent bot building platforms make it possible for a wide range of users to easily create dialogue systems, those platforms don{'}t offer tools for quickly identifying which log dialogues contain problems.
no code implementations • ACL 2020 • Xinnuo Xu, Ond{\v{r}}ej Du{\v{s}}ek, Jingyi Li, Verena Rieser, Ioannis Konstas
Abstractive summarisation is notoriously hard to evaluate since standard word-overlap-based metrics are insufficient.
1 code implementation • Findings (EMNLP) 2021 • Xinnuo Xu, Ondřej Dušek, Shashi Narayan, Verena Rieser, Ioannis Konstas
We show via data analysis that it's not only the models which are to blame: more than 27% of facts mentioned in the gold summaries of MiRANews are better grounded on assisting documents than in the main source articles.
no code implementations • 5 Dec 2023 • Xinnuo Xu, Ivan Titov, Mirella Lapata
Data-to-text generation involves transforming structured data, often represented as predicate-argument tuples, into coherent textual descriptions.
no code implementations • 24 Jan 2024 • Hai X. Pham, Isma Hadji, Xinnuo Xu, Ziedune Degutyte, Jay Rainey, Evangelos Kazakos, Afsaneh Fazly, Georgios Tzimiropoulos, Brais Martinez
The key technological enabler is a novel mechanism for automatic question-answer generation from procedural text which can ingest large amounts of textual instructions and produce exhaustive in-domain QA training data.